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Title: Learning-accelerated discovery of immune-tumour interactions
We present an integrated framework for enabling dynamic exploration of design spaces for cancer immunotherapies with detailed dynamical simulation models on high-performance computing resources. Our framework combines PhysiCell, an open source agent-based simulation platform for cancer and other multicellular systems, and EMEWS, an open source platform for extreme-scale model exploration. We build an agent-based model of immunosurveillance against heterogeneous tumours, which includes spatial dynamics of stochastic tumour–immune contact interactions. We implement active learning and genetic algorithms using high-performance computing workflows to adaptively sample the model parameter space and iteratively discover optimal cancer regression regions within biological and clinical constraints.
Authors:
; ; ; ;
Award ID(s):
1720625
Publication Date:
NSF-PAR ID:
10188156
Journal Name:
Molecular Systems Design & Engineering
Volume:
4
Issue:
4
Page Range or eLocation-ID:
747 to 760
ISSN:
2058-9689
Sponsoring Org:
National Science Foundation
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